Automatic lung lobe segmentation in x-ray CT images by 3D watershed transform using anatomic information from the segmented airway tree

Soumik Ukil; Eric A. Hoffman; Joseph M. Reinhardt

doi:10.1117/12.596752

29 April 2005 Automatic lung lobe segmentation in x-ray CT images by 3D watershed transform using anatomic information from the segmented airway tree

Soumik Ukil, Eric A. Hoffman, Joseph M. Reinhardt

Author Affiliations +

Proceedings Volume 5747, Medical Imaging 2005: Image Processing; (2005) https://doi.org/10.1117/12.596752
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

The human lungs are divided into five distinct anatomic compartments called lobes. The physical boundaries between the lobes are called the lobar fissures. Detection of lobar fissure positions in pulmonary X-ray CT images is of increasing interest for the diagnosis of lung disease. We have developed an automatic method for segmentation of all five lung lobes simultaneously using a 3D watershed transform on the distance transform of a previously generated vessel mask, linearly combined with the original data. Due to the anatomically separate airway sub-trees for individual lobes, we can accurately and automatically place seed points for the watershed segmentation based on the airway tree anatomical description, due to the fact that lower generation airway and vascular tree segments are located near each other. This, along with seed point placement using information on the spatial location of the lobes, can give a close approximation to the actual lobar fissures. The accuracy of the lobar borders is assessed by comparing the automatic segmentation to manually traced lobar boundaries. Averaged over all volumes, the RMS distance errors for the left oblique fissure, right oblique fissure and right horizontal fissure are 3.720 mm, 0.713 mm and 1.109 mm respectively.

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Soumik Ukil, Eric A. Hoffman, and Joseph M. Reinhardt "Automatic lung lobe segmentation in x-ray CT images by 3D watershed transform using anatomic information from the segmented airway tree", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.596752

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